A Two-Step Bayesian Approach for Propensity Score Analysis: Simulations and Case Study

Kaplan, David H.; Chen, Jianshen

doi:10.1007/s11336-012-9262-8

Cited by 48 publications

(123 citation statements)

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“…To maintain a fully Bayesian specification while overcoming the conceptual and practical difficulties of the joint modeling methods of McCandless, Gustafson, and Austin (2009) and An (2010), a two-step Bayesian propensity score approach was recently developed by Kaplan and Chen (2012) that can incorporate prior information on the model parameters of both the propensity score equation and outcome model equation. Consistent with Bayesian theory (see, e.g., De Finetti, 1974), specifying prior distributions on the model parameters is a natural way to quantify uncertainty-here in both the propensity score and outcome equations.…”

Section: The Two-step Bayesian Propensity Score Approachmentioning

confidence: 99%

“…For notational simplicity, let η denote the vector of propensity score model parameters. Kaplan and Chen (2012) then provide the following …”

Section: The Two-step Bayesian Propensity Score Approachmentioning

confidence: 99%

“…Specifically, the propensity score should only be determined by covariates measured prior to treatment implementation and not influenced by the outcome measured after treatment assignment Steiner and Cook (2013). To address this problem, Kaplan and Chen (2012) developed a two-step Bayesian propensity score approach, fitting the propensity score model and outcome model separately and examining its performance in regard to treatment effect and variance estimates via propensity score stratification, weighting, and optimal full matching methods. However, the balance properties of the two-step Bayesian propensity score approach have not yet been evaluated.…”

Section: Introductionmentioning

confidence: 99%

“…This article investigates the balance properties of the two-step Bayesian propensity approach (Kaplan & Chen, 2012) via a case study and a simulation study and demonstrates a way of assessing covariate balance within the Bayesian framework. The link between covariate balance and treatment estimation is also examined and discussed.…”

Section: Introductionmentioning

confidence: 99%

“…Then the following section presents Bayesian propensity score analysis and provides a comprehensive review of the Kaplan and Chen's (2012) two-step Bayesian propensity score approach. Next, the study design as well as results of a case study are presented and discussed, followed by the design and results of a simulation study.…”

Section: Introductionmentioning

confidence: 99%

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Covariate Balance in Bayesian Propensity Score Approaches for Observational Studies

Chen

Kaplan

2015

Journal of Research on Educational Effectiveness

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Bayesian alternatives to frequentist propensity score approaches have recently been proposed. However, few studies have investigated their covariate balancing properties. This article compares a recently developed two-step Bayesian propensity score approach to the frequentist approach with respect to covariate balance. The effects of different priors on covariate balance are evaluated and the differences between frequentist and Bayesian covariate balance are discussed. Results of the case study reveal that both the Bayesian and frequentist propensity score approaches achieve good covariate balance. The frequentist propensity score approach performs slightly better on covariate balance for stratification and weighting methods, whereas the two-step Bayesian approach offers slightly better covariate balance in the optimal full matching method. Results of a comprehensive simulation study reveal that accuracy and precision of prior information on propensity score model parameters do not greatly influence balance performance. Results of the simulation study also show that overall, the optimal full matching method provides the best covariate balance and treatment effect estimates compared to the stratification and weighting methods. A unique feature of covariate balance within Bayesian propensity score analysis is that we can obtain a distribution of balance indices in addition to the point estimates so that the variation in balance indices can be naturally captured to assist in covariate balance checking.

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Section: The Two-step Bayesian Propensity Score Approachmentioning

confidence: 99%

“…For notational simplicity, let η denote the vector of propensity score model parameters. Kaplan and Chen (2012) then provide the following …”

Section: The Two-step Bayesian Propensity Score Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Covariate Balance in Bayesian Propensity Score Approaches for Observational Studies

Chen

Kaplan

2015

Journal of Research on Educational Effectiveness

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Bayesian propensity scores for high‐dimensional causal inference: A comparison of drug‐eluting to bare‐metal coronary stents

Spertus

Normand

2018

Biometrical J

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High-dimensional data provide many potential confounders that may bolster the plausibility of the ignorability assumption in causal inference problems. Propensity score methods are powerful causal inference tools, which are popular in health care research and are particularly useful for high-dimensional data. Recent interest has surrounded a Bayesian treatment of propensity scores in order to flexibly model the treatment assignment mechanism and summarize posterior quantities while incorporating variance from the treatment model. We discuss methods for Bayesian propensity score analysis of binary treatments, focusing on modern methods for high-dimensional Bayesian regression and the propagation of uncertainty. We introduce a novel and simple estimator for the average treatment effect that capitalizes on conjugacy of the beta and binomial distributions. Through simulations, we show the utility of horseshoe priors and Bayesian additive regression trees paired with our new estimator, while demonstrating the importance of including variance from the treatment regression model. An application to cardiac stent data with almost 500 confounders and 9000 patients illustrates approaches and facilitates comparison with existing alternatives. As measured by a falsifiability endpoint, we improved confounder adjustment compared with past observational research of the same problem.

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An approximate Bayesian inference on propensity score estimation under unit nonresponse

Sang

Kim

2021

Can J Statistics

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Nonresponse weighting adjustment using the response propensity score is a popular tool for handling unit nonresponse. Statistical inference after the nonresponse weighting adjustment is an important problem and Taylor linearization method is often used to reflect the effect of estimating the propensity score weights. In this article, we propose an approximate Bayesian approach to handle unit nonresponse with parametric model assumptions on the response probability, but without model assumptions for the outcome variable. The proposed Bayesian method is calibrated to the frequentist inference in that the credible region obtained from the posterior distribution asymptotically matches to the frequentist confidence interval obtained from the Taylor linearization method. The proposed Bayesian method is also extended to incorporate the auxiliary information from full sample. Results from limited simulation studies confirm the validity of the proposed methods. The proposed method is applied to data from a Korean longitudinal survey.

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A Two-Step Bayesian Approach for Propensity Score Analysis: Simulations and Case Study

Cited by 48 publications

References 45 publications

Covariate Balance in Bayesian Propensity Score Approaches for Observational Studies

Covariate Balance in Bayesian Propensity Score Approaches for Observational Studies

Bayesian propensity scores for high‐dimensional causal inference: A comparison of drug‐eluting to bare‐metal coronary stents

An approximate Bayesian inference on propensity score estimation under unit nonresponse

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